基于太赫兹时域光谱技术的煤岩识别方法研究

doi:10.3964/j.issn.1000-0593(2022)06-1755-06

Abstract
References
Related Citation (15)

Download: PDF (2477 KB)
Export: BibTeX | EndNote (RIS)

Abstract Coal-rock identification is one of the key problems restricting unmanned coal mining. Because of the extremely complicated working environment, the traditional manual coal mining is difficult to find the interface of coal and rock accurately, which is easy to cause the phenomenon of undercutting or overcutting. As a non-destructive detection method, Terahertz spectroscopy can reflect the physical and chemical information of the object under test and be an effective method to study the identification of coal and rock. In this paper, the terahertz time-domain spectroscopy and multivariate statistical method-cluster analysis (CA) and principal component analysis (PCA) are used to identify different types of coal and rock. The THz spectra of six coal and rock samples are obtained by transmission terahertz spectrometer. FFT and other mathematical calculations can obtain various samples’ refractive index, absorption coefficient and dielectric constant. The results show differences in the refractive index and absorption coefficient of different types of coal and rock. By analyzing the relationship between the refractive index and absorption coefficient of various coal samples and the content of each component of the samples, it can be found that carbon content is one of the factors affecting the refractive index of the samples, and ash content is one of the factors affecting the absorption coefficient of the samples.The Euclidean distance of two kinds of samples in cluster analysis and the score of PC1 in principal component analysis can reflect the similarity and dissimilarity between coal and rock samples, and the results of CA and PCA are consistent. The refractive index and absorption coefficient of various samples in the 0.5~2.5 THz frequency range are combined with CA and PCA to form a model between terahertz data and coal and rock. According to the analysis,the six types of coal samples in the two models can be divided into two types based on the similarity between different samples. In the CA-PCA model with the absorption coefficient of various samples adopted, four kinds of coal are clustered together. Moreover, quartz sandstone (GSR-4) has a unique characteristic: quartz sandstone has the smallest PC1 score value, and the Euclidian distance between quartz sandstone and the second type is the largest, up to 219.03. It can be seen that the combination of terahertz technology and multivariate statistical method can realize the accurate identification of coal and rock, and the recognition accuracy can reach 100%.

Key words：Terahertz time-domain spectroscopy; Coal-rock identification; Principal component analysis; Cluster analysis

Received: 2021-06-05 Accepted: 2021-08-02

ZTFLH:

O441.6

Corresponding Authors: SHAO Dan E-mail: 191780618@qq.com

Cite this article:

MIAO Shu-guang,SHAO Dan,LIU Zhong-yu, et al. Study on Coal-Rock Identification Method Based on Terahertz Time-Domain Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1755-1760.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)06-1755-06 OR https://www.gpxygpfx.com/EN/Y2022/V42/I06/1755

[1] LI Hui，QI Li-jun，ZHANG Jian-hua（李慧, 祁力钧, 张建华）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报），2012，43(9)：184.
[2] YANG Shuai，ZUO Jian，ZHANG Cun-lin（杨帅, 左剑, 张存林）. Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2016，36(12)：3870.
[3] CHEN Yang，TAN Zuo-jun，XIE Jing, et al（陈阳，谭佐军，谢静，等）. Science and Technology of Food Industry(食品工业科技)，2014，35(14)：49.
[4] YAO Jian-quan（姚建铨）. Journal of Chongqing University of Posts and Telecommunications·Natural Science Edition(重庆邮电大学学报·自然科学版)，2010，22(6)：703.
[5] XU Chang-hong，TENG Xue-ming，ZHAO Hui，et al（许长虹，滕学明，赵卉，等）. Modern Scientific Instruments(现代科学仪器)，2013，(4)：228.
[6] TENG Xue-ming，ZHAO Kun，ZHAO Hui，et al（滕学明，赵昆，赵卉，等）. Modern Scientific Instruments(现代科学仪器)，2011(6)：19.
[7] WANG Xin，ZHAO Duan，HU Ke-xiang，et al（王昕，赵端，胡克想，等）. Journal of China Coal Society(煤炭学报)，2018，43(4)：1146.
[8] LI Peng-peng，ZHANG Yuan，GE Hong-yi，et al（李鹏鹏，张元，葛宏义，等）. Science and Technology of Food Industry(食品工业科技)，2017，38(3)：372.
[9] YU Xian-shu，GAO Lei，LU Gui-wu（于宪书，高磊，卢贵武）. Lubrication Engineering(润滑与密封)，2016，41(12)：26.
[10] WANG Xin，ZHAO Duan，DING En-jie（王昕, 赵端, 丁恩杰）. Coal Mining Technology(煤矿开采)，2018，23(1)：13.
[11] ZHAN Hong-lei，WANG Yu-xia，WANG Xue-song，et al（詹洪磊，王玉霞，王雪松，等）. Journal of Terahertz Science and Electronic Information Technology(太赫兹科学与电子信息学报)，2016，14(1)：26.
[12] Zhan H, Xi J, Zhao K，et al. Food Control，2016，67: 114.
[13] LIU Ling-yu，YANG Chuan-fa，ZHANG Xian-sheng，et al(刘陵玉，杨传法，张献生，等). Journal of China Coal Society(煤炭学报)，2016，41(2)：497.